Where did the data come from?

Brief History of Public Education in TN

TN State Expenditure by Type 2013

TN State Expenditure by Type 2013

Top 2017 High School Rankings Tennesse 2017 High School Ranking

Correlation Matrix of Averaged IRS Data

Correlation Matrix of CORE Education Data

Per Pupil Expenditure

Percentage of Black, Hispanic, and Native American

Graduation Rate

Dropout Rate

Model Prediction of Core Scores Effect on ACT Score

lm(formula = ACT_Composite ~  avg_Eng + avg_Math + avg_Sci + Dropout + Pct_Suspended,  data = merged_irs)
## 
## Call:
## lm(formula = ACT_Composite ~ avg_Eng + avg_Math + avg_Sci + Dropout + 
##     Pct_Suspended, data = merged_irs)
## 
## Coefficients:
##   (Intercept)        avg_Eng       avg_Math        avg_Sci        Dropout  
##     11.114965       0.104072       0.010566       0.020567       0.039315  
## Pct_Suspended  
##      0.003943
          model_multi <- lm(formula = ACT_Composite ~  avg_Eng + avg_Math + avg_Sci, data = merged_irs)
          plot(model_multi)

          summary(model_multi)
## 
## Call:
## lm(formula = ACT_Composite ~ avg_Eng + avg_Math + avg_Sci, data = merged_irs)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2815 -0.5650 -0.1270  0.4809  1.9070 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 12.114030   0.099123 122.212  < 2e-16 ***
## avg_Eng      0.095159   0.004602  20.676  < 2e-16 ***
## avg_Math     0.010742   0.001736   6.190 6.91e-10 ***
## avg_Sci      0.016612   0.003327   4.994 6.29e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7008 on 2801 degrees of freedom
## Multiple R-squared:  0.6806, Adjusted R-squared:  0.6803 
## F-statistic:  1990 on 3 and 2801 DF,  p-value: < 2.2e-16
          # use model to predict the results
          test_model <- data.frame(avg_Eng = 80, avg_Math = 80,  avg_Sci = 80)
          test_model2 <- data.frame(avg_Eng = 90, avg_Math = 90,  avg_Sci = 90)
          test_model3 <- data.frame(avg_Eng = 60, avg_Math = 50,  avg_Sci = 60)
          #test_counts <- model_multi$agi_amt_avg
         
          predict(model_multi, test_model)
##        1 
## 21.91506
          predict(model_multi, test_model2)
##        1 
## 23.14019
          predict(model_multi, test_model3)
##        1 
## 19.35738

\[ACT_Composite = 12.114030 + 0.095159 * avg_Eng + 0.010742 * avg_Math + 0.016612 * avg_Sci\]